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on Knowledge Management and Knowledge Economy |
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Issue of 2026–04–06
five papers chosen by Laura Nicola-Gavrila, Centrul European de Studii Manageriale în Administrarea Afacerilor |
| By: | Wantchekon, Leonard |
| Abstract: | Research and development (R&D) is a central driver of long-term economic growth, technological progress, and institutional capacity. Yet many African countries remain marginal in the global knowledge economy, with limited investment in science, technology, and innovation (STI) and weak research ecosystems. This paper argues that the persistence of Africa's innovation deficit is partly rooted in the design of foreign aid and development policies, which have historically prioritized short-term service delivery over long-term investments in scientific capacity and technological capability. Drawing on economic theory, empirical evidence, and comparative case studies, the paper examines the role of R&D in structural transformation and assesses the structural barriers that limit innovation in Africa, including chronic underfunding, short-term aid cycles, misalignment between donor priorities and national strategies, and weak institutional systems. Evidence from countries such as Ethiopia, Brazil, and China demonstrates how sustained investment in research institutions, human capital, and international knowledge partnerships can generate significant productivity gains and technological upgrading. The paper concludes that development cooperation must shift toward innovation-driven growth. Strengthening universities, financing basic sciences, and fostering university-industry-government collaboration are essential steps for enabling African countries to transition from technology consumers to producers in the global knowledge economy. |
| Keywords: | Foreign aid, development policy, knowledge economy, innovation |
| JEL: | F35 O30 O32 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:ifwkwp:339614 |
| By: | Emanuele Bazzichi; Massimo Riccaboni; Fulvio Castellacci |
| Abstract: | We study how artificial intelligence (AI) affects firms' incentives to pursue incremental versus radical knowledge recombinations. We develop a model of recombinant innovation embedded in a Schumpeterian quality-ladder framework, in which innovation arises from recombining ideas across varying distances in a knowledge space. R&D consists of multiple tasks, a fraction of which can be performed by AI. AI facilitates access to distant knowledge domains, but at the same time it also increases the aggregate rate of creative destruction, shortening the monopoly duration that rewards radical innovations. Moreover, excessive reliance on AI may reduce the originality of research and lead to duplication of research efforts. We obtain three main results. First, higher AI productivity encourages more distant recombinations, if the direct facilitation effect is stronger than the indirect effect due to intensified competition from rivals. Second, the effect of increasing the share of AI-automated R&D tasks is non-monotonic: firms initially target more radical innovations, but beyond a threshold of human-AI complementarity, they shift the focus toward incremental innovations. Third, in the limiting case of full automation, the model predicts that optimal recombination distance collapses to zero, suggesting that fully AI-driven research would undermine the very knowledge creation that it seeks to accelerate. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.02189 |
| By: | Zaggl, Michael A.; Steininger, Dennis M.; Isaak, Andrew J. |
| Abstract: | Performance feedback mechanisms play a pivotal role in motivating users’ voluntary contributions, which are crucial to sustaining online knowledge communities. We use motivation theory and conceptually distinguish reputation and status. Specifically, we hypothesize that reputation motivates contributions when users are of low status, but that achieved status will have a demotivating effect. We test our hypotheses by examining status and reputation mechanisms in a large knowledge exchange community (Stack Overflow). Consistent with our hypotheses, we find robust evidence that reputation-related performance feedback mechanisms are positively related to contribution behavior, whereas status-related mechanisms deplete motivation. Therefore, higher status crowds out the motivational effect of reputation-seeking. This study extends the literature on motivation in knowledge exchange communities by highlighting the difference between status and reputation as two opposing forces. Thereby, we also offer an explanation for the often-observed pattern of declining user contributions in online knowledge exchange communities. |
| Keywords: | Knowledge Exchange, Crowdsourcing, Motivation, Status, Reputation, Panel Regression |
| JEL: | D83 M15 D91 J24 L86 D23 O33 M50 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:esconf:338887 |
| By: | Heinze, Thomas (University of Wuppertal); Achermann, Dania; Dardashti, Radin; Krömer, Ralf; Leuschner, Anna; Morel, Thomas; Overkamp, Anne Sophie; Remmert, Volker; Schiemann, Gregor; Sieben, Anna |
| Abstract: | The DFG Research Training Group 2696 “Transformations of Science and Technology since 1800: Topics, Processes, Institutions” (RTG 2696) at the University of Wuppertal investigates how scientific knowledge and technological capabilities change over time without reducing modes of change to either path dependency or revolutions. Instead, it focuses on transformations as being multidimensional and often gradual-cumulative, while recognizing critical junctures. The program’s shared framework employs varieties of institutionalist approaches (such as Historical Institutionalism, Sociological Institu-tionalism) across three interacting dimensions: topics (theories, concepts, tacit knowledge), processes (experimental, communicative, application-oriented practices), and institutions (universities, laboratories, education systems, infrastructures). Substantively, the RTG addresses field formation and disciplinary change; shifts in epistemic practices; material and technical infrastructures; and narratives of the new and the old, using state-of-the-art methods grounded in history, sociology, and philosophy. These include comparative-historical case studies; qualitative and quantitative methods of empirical social research; conceptual analysis; as well as argumentative reconstruction. Interim results comprise completed dissertations on high-energy physics, medical skills labs, observational standards, and the classification of personality disorders, as well as ongoing projects on the development of academic disciplines (such as mathematics, psychology, economics, and biology), university funding and stratification, space tech-nology, technological persistence, and gendered historiography. Together, these studies aim to test and refine the RTG’s shared vocabulary, enabling synthesis and comparison in the study of transformations (glossary). |
| Date: | 2026–03–24 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:9sg34_v1 |
| By: | Keh-Kuan Sun |
| Abstract: | Generative AI helps users solve problems more efficiently, but without leaving a public trace. Fewer discussions and solutions reach public platforms, and the archives that future problem-solvers depend on can shrink. We build a dynamic model of public good provision where agents contribute by solving problems that other agents posted on a public platform, and the accumulated solutions form a depreciating public archive. AI reduces archive creation through two margins that require different instruments. The flow margin: the posted volume of knowledge-enhancing queries declines as AI resolves more problems privately before they reach the platform. The resolution margin: the probability that posted queries are resolved declines as AI raises contributors' outside options, thinning the contributor pool and creating congestion on the platform. The two margins interact through a self-undermining feedback that can generate low-archive traps. The decomposition yields a diagnostic prediction: in the congested regime, a joint decline in posted volume and conditional resolution requires that supply-side pool thinning is quantitatively present, whereas volume decline with stable or rising resolution indicates that private diversion alone is the dominant force. Encouraging public sharing of AI-assisted solutions offsets the decline associated with private diversion but cannot repair participation-driven deterioration in conditional resolution, which requires maintaining contributor engagement directly. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.00468 |